Anomaly Detection with AI
Detect outliers and anomalies in data using statistical methods, isolation forests, autoencoders, and deep learning approaches. What is Anomaly Detection? Anomaly detection identifies data points that differ significantly from the expected pattern. Applications: fraud detection, network intrusion, manufacturing quality control, and system health monitoring. Anomalies are rare events — often less than 1% of data — making this an imbalanced learning problem. Statistical Methods Z-score detects anomalies as points beyond N standard deviations from the mean. IQR (Interquartile Range) flags points outside Q1-1.5IQR or Q3+1.5IQR. These simple methods work well for univariate normal distributions. For non-normal data, use percentile-based thresholds or the Median Absolute Deviation (MAD) method which is more robust to outliers. Machine Learning Approaches Isolation Forest isolates anomalies by randomly partitioning data — anomalies require fewer splits to isolate. One-Class SVM learns a boundary around normal data. DBSCAN clustering identifies points in low-density regions as anomalies. Autoencoders reconstruct normal data well but fail to reconstruct anomalies (high reconstruction error = anomaly). Deep Learning for Anomalies LSTM autoencoders detect anomalies in time series data by learning normal temporal patterns. The reconstruction error on sequential data flags unusual sequences. Variational Autoencoders (VAEs) provide a probabilistic reconstruction distribution. For high-dimensional data like images or logs, use deep feature extraction followed by density estimation (Gaussian Mixture Models on embeddings).